Prognostic assays for maintenance hemodialysis patients

ABSTRACT

Described herein are methods for determining the overall survival of maintenance hemodialysis patients. The methods include measuring low density lipoprotein (LDL) particle size and subfraction concentrations as prognostic tools for early mortality risk detection. For example, the presence of increased very small LDL concentration or decreased LDL particle size in blood-serum serves as a useful means for prognostic risk assessment and monitoring.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/481,664, filed Apr. 7, 2017, which is a continuation of U.S. patentapplication Ser. No. 13/283,536, filed Oct. 27, 2011, now U.S. Pat. No.9,638,705, which claims benefit of U.S. Provisional Application No.61/409,003, filed Nov. 1, 2010, which is hereby incorporated byreference in its entirety.

STATEMENT OF GOVERNMENT-SPONSORED RESEARCH

This invention was made with United States government support awarded bythe following agencies: National Institutes of Health, NationalInstitute of Diabetes, Digestive and Kidney Disease grants #R21-DK61162and K23-DK061162, and National Centers for Research Resources, NationalInstitutes of Health General Clinical Research Center (GCRC) grant#M01-RR00425. The United States government has certain rights in theinvention.

TECHNICAL FIELD

The present invention generally relates to methods for predicating anddetermining overall survival for maintenance hemodialysis (MHD)patients. The methods described herein include measuring the propertiesof lipoproteins in MHD patients.

BACKGROUND OF THE INVENTION

The following description is provided to assist the understanding of thereader. None of the information provided or references cited is admittedto be prior art.

End stage renal disease (ESRD) is the manifestation of a chronic kidneydisease characterized by complete kidney failure or an imminentprogression thereto. There are a number of treatments for ESRD includinghemodialysis, peritoneal dialysis, and kidney transplantation.Hemodialysis, however, is the primary therapy for patients with ESRD.Approximately seventy percent of ESRD patients require chronic,maintenance hemodialysis (MHD). The aim of MHD treatment is to replacekidney function with long-term hemodialysis intervention. MHD decreasesnephrological degradation by removing contaminants from the blood andmaintaining appropriate blood volume. Hemodialysis is performed by usinga dialysis machine that pumps blood from a patient, through a dialyzer,and then back into the patient. Accordingly, hemodialysis therapy is anextracorporeal process that cleanses a patient's blood.

The number of maintenance dialysis patients in the United States iscurrently over 400,000 and still growing fast. Two thirds of alldialysis patients die within 5 years of initiation of dialysistreatment, a 5-year survival worse than that of many cancers.Approximately half of all dialysis patients die of cardiovasculardisease (CVD). In the general population conventional serum levels ofLDL cholesterol (LDL-C) and HDL cholesterol (HDL-C) predict incidentatherosclerotic CVD. Nevertheless, similar to individuals with chronicheart failure (CHF), the conventional CVD risk factors such ashypercholesterolemia are not associated with mortality in thesepatients; indeed in both dialysis and CHF patients, a low, rather than ahigh, serum total cholesterol (TC) or LDL-C is associated with highermortality, a phenomenon known as lipid paradox or reverse epidemiology.Hence, alternative CVD biomarkers including alternative lipid measuresare needed to more reliably risk-stratify dialysis or CHF patients.

Each lipoprotein class consists of a continuous spectrum of particles ofdifferent size, density, metabolism, and atherogenic impact. Variousstudies have evaluated the associations of small LDL subfractionconcentration, total LDL particle concentration (LDL-Pc), specific HDLsubfractions, and combined measures such as the LDL-C/HDL-C andapoB/apoA-I ratios with cardiovascular risk. However studies on chronickidney disease (CKD) patients are scarce and often limited toconventionally measured TC and LDL-C.

SUMMARY OF THE INVENTION

The present invention provides a method for predicting the overallsurvival of a maintenance hemodialysis patient comprising: measuring oneor both of LDL particle size and LDL subfraction concentration(s) (e.g.,vs-LDL and/or l-LDL) in a sample from the patient, wherein a differencein one or both of LDL particle size and LDL subfraction concentrationcompared to a reference level is an indication of the overall survivalof the patient.

In one aspect, the invention provides a method for predicting mortalityrisk in a maintenance hemodialysis patient by:

-   -   a. measuring the very small low density lipoprotein (vs-LDL)        concentration in a sample from the patient,    -   b. comparing the sample vs-LDL concentration measured in        step (a) with a reference vs-LDL concentration, and    -   c. identifying the patient as having increased risk of mortality        when the sample vs-LDL concentration is greater than the        reference vs-LDL concentration or identifying the patient as        having no change in the risk of mortality when the sample vs-LDL        concentration is less than or equal to the reference vs-LDL        concentration.

In another aspect, the invention provides a method for predictingmortality risk in a maintenance hemodialysis patient by:

-   -   a. measuring the large low density lipoprotein (l-LDL)        concentration in a sample from the patient,    -   b. comparing the sample l-LDL concentration measured in step (a)        with a reference l-LDL concentration, and    -   c. identifying the patient as having reduced risk of mortality        when the sample l-LDL concentration is greater than the        reference l-LDL concentration or identifying the patient as        having no change in the risk of mortality when the sample l-LDL        concentration is less than the reference l-LDL concentration.

In another aspect, the invention provides a method for predictingmortality risk in a maintenance hemodialysis patient by:

-   -   a. measuring the very small low density lipoprotein (vs-LDL)        concentration and the large low density lipoprotein (l-LDL)        concentration in a sample from the patient,    -   b. comparing the sample vs-LDL concentration measured in        step (a) with a reference vs-LDL concentration and comparing the        sample l-LDL concentration measured in step (a) with a reference        l-LDL concentration,    -   c. identifying the patient as having        -   i. increased risk of mortality when the sample vs-LDL            concentration is greater than the reference vs-LDL            concentration,        -   ii. reduced risk of mortality when the sample l-LDL            concentration is greater than the reference l-LDL            concentration, or        -   iii. no change in the risk of mortality when the sample            vs-LDL concentration is less than the reference vs-LDL            concentration and the l-LDL concentration is less than or            equal to the reference l-LDL concentration.

In one embodiment of any of the foregoing aspects, the reference vs-LDLconcentration is about 121 nmol/L and or the reference l-LDLconcentration is about 105 nmol/L. In other embodiments, either one orboth of the reference vs-LDL concentration and reference l-LDLconcentration are derived from a control population of subjects notundergoing maintenance hemodialysis. Alternatively, one or both of thereference vs-LDL concentration and reference l-LDL concentration are theconcentrations measured in the same patient at an earlier time or in thesame patient prior to receiving treatment.

In another aspect, the invention provides a method for predictingmortality risk in a maintenance hemodialysis patient comprising:

-   -   a. measuring the low density lipoprotein (LDL) particle size in        a sample from the patient,    -   b. comparing the sample LDL particle size measured in step (a)        with a reference LDL particle size, and    -   c. identifying the patient as having increased risk of mortality        when the sample LDL particle size is less than the reference LDL        particle size or identifying the patient as having no change in        the risk of mortality when the sample LDL particle size is        greater than or equal to the reference LDL particle size.

In one embodiment, the measured LDL particle size is the mean LDLparticle size. Preferably, the reference mean LDL particle size withinthe range of about 216 Å to about 224 Å. In some embodiments, thereference mean LDL particle size is at least about 216 Å, 218 Å, 219 Å,220 Å, 222 Å, 224 Å, or more. The reference LDL particle size may bederived from a control population of subjects not undergoing maintenancehemodialysis. Alternatively, the reference LDL particle size is the LDLparticle size measured in the same patient at an earlier time or in thesame patient prior to receiving treatment.

In another aspect, the invention provides a method for predictingmortality risk in a maintenance hemodialysis patient by:

-   -   a. measuring the high density lipoprotein (HDL) concentration in        a sample from the patient,    -   b. comparing the sample HDL concentration measured in step (a)        with a reference vs-HDL concentration, and    -   c. identifying the patient as having increased risk of mortality        when the sample HDL concentration is greater than the reference        HDL concentration or identifying the patient as having no change        in the risk of mortality when the sample HDL concentration is        less than or equal to the reference HDL concentration.

In some embodiments, the reference HDL concentration is in the range ofabout 2,000-3,500 nmol/L, about 2,500-3,300 nmol/L, about 2,800-3,100nmol/L, or about 2850-3000 nmol/L. In some embodiments, the referenceHDL concentration is about 2,500, 2,600, 2,700, 2,800, 2,900, 2,919,3,000, 3,100, 3,200, 3,300 nmol/L or more. In other embodiments, thereference HDL concentration is derived from a control population ofsubjects not undergoing maintenance hemodialysis. Alternatively, thereference HDL concentration is the concentration measured in the samepatient at an earlier time or in the same patient prior to receivingtreatment.

In another aspect, the invention provides a method for predictingmortality risk in a maintenance hemodialysis patient by:

-   -   a. measuring the large low density lipoprotein (l-LDL) particle        concentration and the small low density lipoprotein (s-LDL)        particle concentration in a sample from the patient,    -   b. determining the ratio of the l-LDL concentration to the s-LDL        concentration, and    -   c. identifying the patient as having reduced risk of mortality        when the sample l-LDL:s-LDL ratio is greater than the reference        ratio or identifying the patient as having no change in the risk        of mortality when the reference l-LDL:s-LDL ratio is less than        the reference ratio.

In some embodiments, the reference l-LDL:s-LDL ratio is in the range ofabout 0.75-1.25, or about 0.80-1.15, or about 0.85-1.10, or about0.90-1.05, or about 0.95-1.00. In some embodiments, the referencel-LDL:s-LDL ratio is about 0.75, 0.80, 0.85, 0.90, 0.95, 0.97, 1.00,1.05, 1.10, 1.15, 1.20, 1.25, or more. In other embodiments, thereference l-LDL:s-LDL ratio is derived from a control population ofsubjects not undergoing maintenance hemodialysis. Alternatively, thereference l-LDL:s-LDL ratio is the ratio measured in the same patient atan earlier time or in the same patient prior to receiving treatment.

In other embodiments of any of the foregoing aspects, the sample iswhole blood, serum, or plasma. The sample LDL and HDL concentrations,including the subfraction concentrations (e.g., vs-LDL concentration andl-LDL concentration) and/or the LDL particle size may be measured by ionmobility analysis.

Optionally, the methods of the invention may further comprising the stepof selecting a treatment regime based upon mortality risk of thepatient.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1A and FIG. 1B are a series of scatter plots (including theregression line and 95% confidence intervals) showing the correlationsof serum LDL (FIG. 1A) and HDL (FIG. 1B) cholesterol concentrations withlarge LDL and HDL subfraction concentrations.

FIG. 2A and FIG. 2B are a series of cubic spline models of the Coxproportional regression analyses reflecting adjustedmortality-predictability (with 95% CI) according to the LDL-P diameterin the entire cohort of 235 maintenance MHD patients over 5 years (fromOct. 2001 to Jan. 2007), as discussed in Example 3. Spline models arewith 2 degrees of freedom. (FIG. 2A) unadjusted; (FIG. 2B), adjusted forcase-mix+lipids+MICS+inflammation. Case-mix included age, gender,race/ethnicity, diabetes mellitus, dialysis vintage, modified Charlsoncomorbidity score and dialysis dose (single pool Kt/V). Lipids includedLDL and HDL cholesterol concentration and TG. Malnutrition-inflammationcomplex syndrome (MICS) variables included serum or blood levels ofphosphorus, albumin, creatinine, calcium, ferritin, hemoglobin,normalized protein catabolic rate (nPCR), also known as normalizedprotein nitrogen appearance (nPNA); and body mass index. Inflammatorymarkers include serum concentrations of C-reactive protein (CRP), IL-6,and TNF-α.

FIG. 3 is a bar graph showing the death hazard ratio according tocategories (above median (AM) or below median (BM)) of LDL-C and LDL-Pdiameter after full adjustment. Median values for LDL-C and LDL-Pd are73 mg/dL and 216.5 A, respectively. Full adjustment means adjustment forcase-mix (age, gender, race/ethnicity, diabetes mellitus, dialysisvintage, modified Charlson comorbidity score and dialysis dose (singlepool Kt/V)) and lipids (TG, LDL and HDL particle concentration), MICS(serum or blood levels of phosphorus, albumin, creatinine, calcium,ferritin, hemoglobin, normalized protein catabolic rate (nPCR), alsoknown as normalized protein nitrogen appearance (nPNA); and body massindex) and inflammation (CRP, IL6, TNF-α).

FIG. 4 is a line graph showing the Kaplan-Meier proportion of survivingMHD patients after 5 years of observation according to the categories ofLDL cholesterol concentration and LDL particle diameter in 235 MHDpatients.

DETAILED DESCRIPTION

The present inventions are based on the identification that, inmaintenance hemodialysis (MHD) patients, smaller sized LDL-Pd and higherconcentrations of very small LDL-P are associated with increasedmortality, whereas higher concentration of larger sized LDL-P isassociated with decreased risk of death. Accordingly, in some aspects,the invention provides methods for determining overall survival in MHDpatients based at least partially on lipoprotein analysis of MHD patientsamples. Further disclosed herein are methods for predicting mortalitybased on the measurement of lipoprotein properties from a sample.Specifically, the present invention generally describes for themeasurement of LDL particle size and subfraction concentration in MHDpatients.

As used in this specification and the appended claims, the singularforms “a”, “an”, and “the” include plural referents unless the contentclearly dictates otherwise. For example, reference to “a nucleic acid”includes a combination of two or more nucleic acids, and the like.

As used herein, “about” will be understood by persons of ordinary skillin the art and will vary to some extent depending upon the context inwhich it is used. For example, when referring to the mean LDL particlesize, “about” refers to ±1-5 ∈. When referring to LDL concentrations,“about” will mean up to ±5-10% of the enumerated value.

As used herein, the term “biomarker” or “LDL property” or “properties ofLDL” in the context of the present invention refers to a LDLconcentration, particle size, or combination thereof, or a LDLsubfraction concentration, particle size, or combination thereof,including, but not limited to, large LDL and very small LDL, which isdifferentially present in a sample taken from MHD patients as comparedto a comparable sample taken from a control subject or a population ofcontrol subjects, or as compared to a reference level or value.

The term “clinical factors” as used herein, refers to any data that amedical practitioner may consider in determining a diagnosis orprognosis of disease. Such factors include, but are not limited to, thepatient's medical history, a physical examination of the patient,complete blood count, etc.

The term “comparable” or “corresponding” in the context of comparing twoor more samples, means that the same type of sample, e.g., whole blood,is used in the comparison. For example, a LDL level in a sample of wholeblood can be compared to a LDL level in another whole blood sample. Insome embodiments, comparable samples may be obtained from the sameindividual at different times. In other embodiments, comparable samplesmay be obtained from different individuals, e.g., a patient and ahealthy individual. In general, comparable samples are normalized by acommon factor. For example, body fluid samples are typically normalizedby volume body fluid and cell-containing samples are normalized byprotein content or cell count.

As used herein, the term “control population” refers to individuals notundergoing maintenance hemodialysis or with a negative diagnosis orundetectable kidney disease, i.e., normal or healthy subjects.

The terms “determining,” “measuring,” “assessing,” and “assaying” areused interchangeably and include both quantitative and qualitativedeterminations. These terms refer to any form of measurement, andinclude determining if a characteristic, trait, or feature is present ornot. Assessing may be relative or absolute. “Assessing the presence of”includes determining the amount of something present, as well asdetermining whether it is present or absent.

As used herein, the term “diagnosis” means detecting a disease ordisorder or determining the stage or degree of a disease or disorder.Usually, a diagnosis of a disease or disorder is based on the evaluationof one or more factors and/or symptoms that are indicative of thedisease. That is, a diagnosis can be made based on the presence, absenceor amount of a factor which is indicative of presence or absence of thedisease or condition. Each factor or symptom that is considered to beindicative for the diagnosis of a particular disease does not need beexclusively related to the particular disease; i.e., there may bedifferential diagnoses that can be inferred from a diagnostic factor orsymptom. Likewise, there may be instances where a factor or symptom thatis indicative of a particular disease is present in an individual thatdoes not have the particular disease. The term “diagnosis” alsoencompasses determining the therapeutic effect of a drug therapy, orpredicting the pattern of response to a drug therapy. The diagnosticmethods may be used independently, or in combination with otherdiagnosing and/or staging methods known in the medical arts for aparticular disease or disorder, e.g., end stage kidney disease, whereina patient is undergoing MHD.

As used herein, the phrase “difference of the level” or “difference inproperties” refers to differences in the properties or a quantity of anLDL biomarker present in a sample taken from MHD patients as compared toa control. In one embodiment, a biomarker can be a LDL property or levelwhich is present at an elevated amount or at a decreased amount insamples of MHD patients compared to a reference level.

As used herein, the term “ion mobility analysis” or “IMA” refers to themeasurement of non-covalently bound particles that are processed througha system while maintaining their properties. For example, a sample isplaced in a pressurized chamber of an IMA apparatus. Subsequently, thesample is exposed to dry gas and alpha radiation, thereby forming singlycharged droplets. In one embodiment, the droplets are the particles thatare measured. Once a relationship is determined between particle sizeand density, size or mobility distributions can then be converted intodistributions of particle mass, density (μg/cm³), and/or concentration,and the like. Accordingly, IMA allows for particle size andconcentration to be determined in a sample of lipoprotein particles,e.g., VLDL, IDL, LDL, HDL and their subclasses and/or subfractions.

As used herein, the term “lipoprotein” or “lipoprotein particle” referto particles obtained from mammalian blood which include apolipoproteinsbiologically assembled with noncovalent bonds to package for example,without limitation, cholesterol and other lipids. Lipoproteins typicallyrefer to biological particles having various sizes, and include very lowdensity lipoproteins (VLDL), intermediate density lipoproteins (IDL),low density lipoproteins (LDL), lipoprotein (a), high densitylipoproteins (HDL) and chylomicrons.

As used herein, “VLDL”, “IDL”, “LDL”, and “HDL” refer to classificationsof lipoproteins. It is understood that the values for particle diametermay be determined by gel electrophoresis methods, as known in the art,or mobility analysis methods. With ion mobility analysis methods it hasbeen observed that lipoprotein diameters can be smaller relative todiameters obtained with gel electrophoresis.

As used herein, the term “population” may be any group of at least twoindividuals. A population may include, e.g., but is not limited to, acontrol population, a patient population, a reference population, apopulation group, a family population, a clinical population, and a samesex population.

As used herein, the term “overall survival” or “OS” is used to refer totime in years from surgery to death from any cause. The calculation ofthis measure may vary depending on the definition of events to be eithercensored or not considered.

The term “prognosis” as used herein refers to a prediction of theprobable course and outcome of a clinical condition or disease. Aprognosis is usually made by evaluating factors or symptoms of a diseasethat are indicative of a favorable or unfavorable course or outcome ofthe disease. The phrase “determining the prognosis” as used hereinrefers to the process by which the skilled artisan can predict thecourse or outcome of a condition in a patient. The term “prognosis” doesnot refer to the ability to predict the course or outcome of a conditionwith 100% accuracy. Instead, the skilled artisan will understand thatthe term “prognosis” refers to an increased probability that a certaincourse or outcome will occur; that is, that a course or outcome is morelikely to occur in a patient exhibiting a given condition, when comparedto those individuals not exhibiting the condition. The terms “favorableprognosis” and “positive prognosis,” or “unfavorable prognosis” and“negative prognosis” as used herein are relative terms for theprediction of the probable course and/or likely outcome of a conditionor a disease. A favorable or positive prognosis predicts a betteroutcome for a condition than an unfavorable or negative prognosis. In ageneral sense, a “favorable prognosis” is an outcome that is relativelybetter than many other possible prognoses that could be associated witha particular condition, whereas an unfavorable prognosis predicts anoutcome that is relatively worse than many other possible prognoses thatcould be associated with a particular condition.

As used herein, the term “reference level” refers to a level of asubstance which may be of interest for comparative purposes. In oneembodiment, a reference level may be the size or concentration of alipoprotein expressed as an average of the size or concentration of alipoprotein from samples taken from a control population of healthy(disease-free) subjects. In one embodiment, the reference level may bethe level in the same subject at a different time, e.g., before thepresent assay, such as the level determined prior to the subjectdeveloping the disease or prior to initiating therapy. In general,samples are normalized by a common factor. For example, body fluidsamples are normalized by volume body fluid and protein orcell-containing samples are normalized by protein content or the like.

As used herein, the term “sample” or “test sample” refers to any liquidor solid material containing lipoproteins. In one embodiment, a sampleis obtained from a biological source, i.e., a “biological sample”, suchas blood or a fluid sample from an animal, most preferably, a human.

As used herein, the term “subject” refers to a mammal, such as a human,but can also be another animal such as a domestic animal, e.g., a dog,cat, or the like, a farm animal, e.g., a cow, a sheep, a pig, a horse,or the like, or a laboratory animal, e.g., a monkey, a rat, a mouse, arabbit, a guinea pig, or the like. The term “patient” refers to a“subject” who is, or is suspected to be, on MHD or afflicted with ESRD.

The phrase “substantially the same as” in reference to a comparison ofone value to another value for the purposes of clinical management of adisease or disorder means that the values are statistically notdifferent. Differences between the values can vary, for example, onevalue may be within 20%, within 10%, within 5%, within 2.5%, or within1% of the other value.

As used herein, the terms “treating” or “treatment” or “alleviation”refers to both therapeutic treatment and prophylactic or preventativemeasures, wherein the object is to prevent or slow down (lessen) thetargeted pathologic condition or disorder. A subject is successfully“treated” for a disorder if, after receiving a therapeutic agentaccording to the methods of the present invention, the subject showsobservable and/or measurable reduction in or absence of one or moresigns and symptoms of a particular disease or condition.

Sample Collection, Preparation, and Purification

The methods described herein provide for the measurement of LDLproperties from a sample obtained from a MHD patient. Samples may beobtained using standard procedures and can be employed immediately orstored, under conditions appropriate for the type of sample, for lateruse. Following collection and preparation, the sample is subjected toanalysis of lipoproteins, wherein the resulting LDL measurements allowfor the prognostic determination of a MEM patient's overall survival.

The starting material for the methods described herein is typically aclinical sample, which is obtained from a MHD patient. Subsequently, LDLis separated from proteins and other biological constituents in theoriginal sample. Purification methods known in the art may be used inthe context of the present invention.

Methods for obtaining samples are well known to those of skill in theart and include, but are not limited to, aspirations, tissue sections,swabs, drawing of blood or other fluids, surgical or needle biopsies,and the like. The sample may be obtained from a subject, individual, orpatient. The sample may contain cells, tissues or fluid obtained from aMHD patient. The sample may be a cell-containing liquid or a tissue.Samples may include, but are not limited to, biopsies, blood, bloodcells, bone marrow, fine needle biopsy samples, peritoneal fluid,amniotic fluid, plasma, pleural fluid, saliva, semen, serum, tissue ortissue homogenates, frozen or paraffin sections of tissue. Samples mayalso be processed, such as sectioning of tissues, fractionation,purification, or cellular organelle separation.

In one embodiment, the lipoproteins derive from a plasma or serumsample, that has been purified. In one embodiment, plasma is the fluidobtained upon separating whole blood into solid and liquid components.In one embodiment, serum is the fluid obtained upon separating wholeblood into solid and liquid components after it has been allowed toclot. In one embodiment, the plasma and/or serum is obtained from a MHDpatient. In one embodiment, the plasma and/or serum, is obtained bywithdrawing or otherwise collecting a biological tissue or fluidincluding, e.g., whole blood, serum and plasma.

In one embodiment, the sample is purified prior to analysis. To thisend, the methods provide for isolation and/or purification oflipoproteins, initial sample collection, and preparation. In oneembodiment, a 2 to 5 ml fasting blood specimen is initially obtained.Chylomicrons are not typically present in subjects who have been fastingfor a period of at least 12 hours; thus, chylomicrons are eliminated byfasting. In one embodiment, the specimen is then subjected tocentrifugation, via a clinical centrifuge, for approximately 10 minutes(min) at approximately 2000× G. In one embodiment, the centrifugation issufficient to remove the cellular debris, i.e., unwanted cellularcomponents, from the specimen. During this process, the more densecellular components stratify at the bottom of the sample. A remainingless dense plasma specimen containing lipoproteins on top is then drawnoff using methods well known in the art, e.g., aspiration.

In one embodiment, in preparation for centrifugation, a sample, e.g.,plasma specimen, is adjusted to a specific density using high puritysolutions or solids of inorganic salts, e.g., sodium chloride (NaCl),sodium bromide (NaBr), and the like. In one embodiment, the specificdensity is chosen to be greater than or equal to the highest density ofthe lipoprotein material to be analyzed, i.e., so that the lipoproteinmaterial floats subsequent to density stratification. In one embodiment,the adjusted sample is ultracentrifuged, e.g., for approximately 18hours (h) at 100,000× G, thereby separating the non-lipoprotein proteinsfrom the lipoproteins. Non-lipoprotein proteins are removed from asample, e.g., the plasma specimen via ultracentrifugation. Accordingly,the lipoproteins float to the top of the sample duringultracentrifugation. Thus, by sequentially centrifuging from the lowestdensity to highest density of the adjusted sample, the various classesand subclasses of lipoproteins can be sequentially extracted. In oneembodiment, a dialysis step follows the extraction of a centrifugedsample to remove any salts that were previously introduced to thesample. In one embodiment, the dialysis step is performed for 4-12 hunder conditions well known in the art.

In one embodiment, conditions for centrifugation forlipoprotein-containing samples described herein are well known in theart of biochemical separation. For example, samples are typicallycentrifuged at 10° C. for 1-4 h at 223,000× G. In one embodiment,centrifugation employs centrifugal force of between about50,000-100,000, 100,000-120,000, 120,000-150,000, 150,000-200,000,200,000-230,000, 230,000-250,000× G, or even higher force. In oneembodiment, the time of centrifugation is about 1, 2, 2.2, 2.4, 2.6,2.8, 3.0, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4, 5, 6, 7, 8, 9,10, 11, 12, 13, 14, 15, 16, 17, 18 h, or even longer. In one embodiment,prior to analysis by ion mobility, an aliquot of the lipid fraction isremoved, e.g., 10-200 μL, from the top of the centrifuge tube anddiluted, e.g., 1:800, in 25 mM ammonium acetate, 0.5 mM ammoniumhydroxide, at a pH 7.4.

In one embodiment, the density of the solution is a value less than orequal to about 1.21 g/mL while centrifuging, this improves recovery,hence purification, of LDL. In one embodiment, the lipoproteins that aresubjected to further purification are selected from the group consistingof HDL, LDL, IDL, Lp(a), and VLDL. In one embodiment, the lipoproteinsare LDL, including subfractions thereof

In one aspect, the present invention provides methods for analyzing theconcentration and/or size distribution of lipoproteins, lipoproteinparticles, and lipoprotein subfractions, e.g., LDL, LDL particle size,very small LDL concentration, and large LDL concentration, by ionmobility analysis. In one embodiment, LDL particle size includes, but isnot limited to, sizes between about 0-1000, 0-500, 100-400, 150-350,200-250, and 215-230 angstroms (Å). In one embodiment, very small LDL isa subfraction of total LDL. In one embodiment, very small LDL is LDLthat is less than about 230, 225, 220, 215, 210, or 205 Å. In oneembodiment, very small LDL is LDL that is less than about 219 Å. In oneembodiment, large LDL is a subfraction of total LDL. In one embodiment,large LDL is greater than about 220, 225, 230, 235, 240, or 245 Å. Inone embodiment, large LDL is LDL that is greater than about 230 Å.

In one aspect, ion mobility analysis provides for analyzing particles inaerosols, biological solutions, or the like. Ion mobility analysis hasbeen adapted to analyze various large and small biologicalmacromolecules. Moreover, ion mobility analysis can be adapted tomeasure various biological particles as described herein, and aspreviously described. See e.g., U.S. Pat. Nos. 7,514,213; 7,259,018;7,713,744; and U.S. Pat. App. Pub. Nos.: 2008/0305549; 2008/0302666; and2003/0136680, all of which are herein incorporated by reference in theirentirety. Ion mobility analysis is also known as ion electrical mobilityor charged-particle mobility.

In one embodiment, analysis of biological particles includes employingan ion mobility analysis apparatus. Following ultracentrifugation, asample is placed into a pressurized chamber. In one embodiment, a highvoltage variable power supply positively biases the sample in thepressurized chamber. The positive bias and higher relative pressureallows for particle droplets to emit from the pressurized chamber. Oncethe droplets are formed, a dry gas is employed to propel the dropletsinto an emission region of an alpha radiation source, thereby reducingthe charge state of the droplets to no more than one positive charge perdroplet. In one embodiment, the charged droplets may be obtained usingother methods to achieve a uniform charge state of no more than a singlepositive charge. In one embodiment, an alternating current corona, whichproduces secondary electrons having the same charge state reduction asan alpha source, is employed.

After charge reduction, the dry gas propels the particles into adifferential mobility analyzer. A laminar flow excess gas, and anadditional dry gas flow, is introduced into the analyzer to match thevelocity of the dry gas flow. In one embodiment, varying the highvoltage power supply allows for the particles, carried by the combinedflows, to be incorporated into a mobility selected particle flow, whichin turn flows into a particle counter. In one embodiment, the particlecounter analyzes particle mobility and corresponding particle size. Theparticle counter is typically linked with a computer system for furtheranalysis and data storage.

In one embodiment, ion mobility analysis allows for non-covalently boundbiological particles to be processed through a system without losingtheir biological properties or degrading. In one embodiment, lipoproteinparticles, e.g., VLDL, IDL, LDL, HDL and their subclasses and/orsubfractions, are rapidly processed, thereby avoiding lipoproteinparticle degradation. Once a relationship is known between particle sizeand density, size or mobility distributions are then converted intodistributions of: particle mass; density μg/cm³ of original plasma;number of particles in a size interval; and/or amount of particle massin a size interval. Accordingly, ion mobility analysis allows for LDLparticle size and subfraction concentrations to be determined in asample.

In one aspect, analysis of biological particles is by gelelectrophoresis. Gel electrophoresis is a technique used to separatecharged molecules or particles according to their physical properties,i.e., charge or mass. As the particles are forced through a sieving gelmatrix by an electrical current, they are separated based on their sizeand charge. Following ultracentrifugation, a sample containinglipoproteins is separated according to particle size using gelelectrophoresis. In one embodiment, nondenaturing gradient gelelectrophoresis is employed. Gradient gels employ an increasingpercentage of matrix, e.g., polyacrylamide, in one direction to allowfor increased separation of particles with similar properties. In oneembodiment, variable rate density gradient gel electrophoresis isemployed. See e.g., U.S. Pat. No. 5,925,229. In one embodiment,calibration markers are concomitantly separated on the gel, therebyallowing for the determination of LDL particle size. In one embodiment,due to the small variations in the sphericity among lipoprotein species,the migration distance of each particle is inversely related to particlesize. It is well known in the art that polyacrylamide or agarose gelelectrophoresis may be employed, at various percentages, for suitableseparation of molecules or particles. Lipoprotein concentration, definedby particle size relative to the calibration marker, is determined byintegration of the area of its peak on a densitometry scan. Accordingly,a corresponding particle size and subfraction concentration can becalculated for a lipoprotein, i.e., LDL or a subfraction thereof.

Determining Prognosis

A prognosis may be expressed as the amount of time a patient can beexpected to survive. Alternatively, a prognosis may refer to thelikelihood that the disease goes into remission or to the amount of timethe disease can be expected to remain in remission. Prognosis can beexpressed in various ways; for example, prognosis can be expressed as apercent chance that a patient will survive after one year, five years,ten years or the like. Alternatively, prognosis may be expressed as thenumber of years, on average that a patient can expect to survive as aresult of a condition or disease. The prognosis of a patient may beconsidered as an expression of relativism, with many factors affectingthe ultimate outcome. For example, for patients with certain conditions,prognosis can be appropriately expressed as the likelihood that acondition may be treatable or curable, or the likelihood that a diseasewill go into remission, whereas for patients with more severe conditionsprognosis may be more appropriately expressed as likelihood of survivalfor a specified period of time.

Additionally, a change in a clinical factor from a baseline level mayimpact a patient's prognosis, and the degree of change in level of theclinical factor may be related to the severity of adverse events.Statistical significance is often determined by comparing two or morepopulations, and determining a confidence interval and/or a p value.

Multiple determinations of LDL particle size and/or LDL subfractionconcentration levels can be made, and a temporal change in activity canbe used to determine a prognosis. For example, comparative measurementsare made of the LDL particle size or LDL subfraction concentration of abody fluid in a patient at multiple time points, and a comparison of avalue at two or more time points may be indicative of a particularprognosis.

A prognosis is often determined by examining one or more clinicalfactors and/or symptoms that correlate to patient outcomes. As describedherein, the LDL particle size and/or LDL subfraction concentration is aclinical factor useful in determining prognosis. The skilled artisanwill understand that associating a clinical factor with a predispositionto an adverse outcome may involve statistical analysis.

In certain embodiments, the levels of LDL particle size and/or LDLsubfraction concentration are used as indicators of an unfavorableprognosis. According to the method, the determination of prognosis canbe performed by comparing the measured LDL particle size and/or LDLsubfraction concentration level to levels determined in comparablesamples from healthy individuals or to levels known to correspondingwith favorable or unfavorable outcomes. The absolute levels obtained maydepend on an number of factors, including, but not limited to, thelaboratory performing the assays, the assay methods used, the type ofbody fluid sample used and the type of disease a patient is afflictedwith. According to the method, values can be collected from a series ofpatients with a particular disorder to determine appropriate referenceranges. One of ordinary skill in the art is capable of performing aretrospective study that compares the determined LDL particle sizeand/or LDL subfraction concentration levels to the observed outcome ofthe patients and establishing ranges of levels that can be used todesignate the prognosis of the patients with a particular disorder. Forexample, LDL particle size and/or LDL subfraction concentration levelsin the lowest range would be indicative of a more favorable prognosis,while LDL particle size and/or LDL subfraction concentration levels inthe highest ranges would be indicative of an unfavorable prognosis.Thus, in this aspect the term “elevated levels” refers to levels of LDLparticle size and/or LDL subfraction concentration that are above therange of the reference value. In some embodiments patients with “high”or “elevated” LDL particle size and/or LDL subfraction concentrationlevels have levels that are higher than the median activity in apopulation of patients with that disease. In certain embodiments, “high”or “elevated” LDL particle size and/or LDL subfraction concentrationlevels for a patient with a particular disease refers to levels that areabove the median values for patients with that disorder and are in theupper 40% of patients with the disorder, or to levels that are in theupper 20% of patients with the disorder, or to levels that are in theupper 10% of patients with the disorder, or to levels that are in theupper 5% of patients with the disorder.

Because the level of LDL particle size and/or LDL subfractionconcentration in a test sample from a patient may relate to theprognosis of a patient in a continuous fashion, the determination ofprognosis can be performed using statistical analyses to relate thedetermined levels to the prognosis of the patient. A skilled artisan iscapable of designing appropriate statistical methods. For example, themethods may employ the chi-squared test, the Kaplan-Meier method, thelog-rank test, multivariate logistic regression analysis, Cox'sproportional-hazard model and the like in determining the prognosis.Computers and computer software programs may be used in organizing dataand performing statistical analyses.

In certain embodiments, the prognosis of MEM patients can be correlatedto the clinical outcome of the disease using the LDL particle sizeand/or LDL subfraction concentration level and other clinical factors.Simple algorithms have been described and are readily adapted to thisend. The approach by Giles et. al., British Journal of Hemotology,121:578-585, is exemplary. As in Giles et al., associations betweencategorical variables (e.g., LDL particle size and/or LDL subfractionconcentration and clinical characteristics) can be assessed viacrosstabulation and Fisher's exact test. Unadjusted survivalprobabilities can be estimated using the method of Kaplan and Meier. TheCox proportional hazards regression model also can be used to assess theability of patient characteristics (such as LDL particle size and/or LDLsubfraction concentration levels) to predict survival, with ‘goodness offit’ assessed by the Grambsch-Therneau test, Schoenfeld residual plots,martingale residual plots and likelihood ratio statistics (see Grambschet al, 1995). In some embodiments, this approach can be adapted as asimple computer program that can be used with personal computers orpersonal digital assistants (PDA). The prediction of patients' survivaltime in based on their LDL particle size and/or LDL subfractionconcentration levels can be performed via the use of a visual basic forapplications (VBA) computer program developed within Microsoft® Excel.The core construction and analysis may be based on the Cox proportionalhazard models. The VBA application can be developed by obtaining a basehazard rate and parameter estimates. These statistical analyses can beperformed using a statistical program such as the SAS® proportionalhazards regression, PHREG, procedure. Estimates can then be used toobtain probabilities of surviving from one to 24 months given thepatient's covariates. The program can make use of estimatedprobabilities to create a graphical representation of a given patient'spredicted survival curve. In certain embodiments, the program alsoprovides 6-month, 1-year and 18-month survival probabilities. Agraphical interface can be used to input patient characteristics in auser-friendly manner.

In some embodiments of the invention, multiple prognostic factors,including LDL particle size and/or LDL subfraction concentration level,are considered when determining the prognosis of a patient. For example,the prognosis of a cancer patient may be determined based on LDLparticle size and/or LDL subfraction concentration and one or moreprognostic factors selected from the group consisting of status, age,gender and previous diagnosis. In certain embodiments, other prognosticfactors may be combined with the LDL particle size and/or LDLsubfraction concentration level in the algorithm to determine prognosiswith greater accuracy.

MHD Prognosis Based on LDL Size

Disclosed herein are methods for predicting overall survival in a MEMpatient based on LDL particle size analysis. Further disclosed hereinare methods for monitoring the status of MHD patients based on LDLparticle size analysis. In one embodiment, predicting the overallsurvival and monitoring the status of MHD patients requires a sampletherefrom. The samples disclosed herein are represented by, but notlimited to, whole blood, plasma, and/or serum. The present inventionrelates to methods for predicating overall survival and monitoring thestatus of MHD patients via comparing LDL particle size in a sample froma patient to a reference level or control population.

In one embodiment, a sample is obtained from a MHD patient.Subsequently, LDL particle size is measured in the sample by ionmobility analysis. In one embodiment, overall survival or mortality riskis then determined by comparing the patient's LDL particle size to areference level or control population. In one embodiment, a differencebetween the patient's LDL particle size and the reference levelindicates an increased risk of mortality.

In one embodiment, the LDL particle size of the reference level or theLDL particle size of a control population is between about 216-230 orbetween 216-224 Å. In one embodiment, the LDL particle size of thereference level or the LDL particle size of a control population isbetween about 216-222 Å, and preferably about 216 Å.

In one embodiment, LDL particle size percentage difference between a MHDpatient and the reference level or the control population is indicativeof an increased risk of mortality, i.e., a decreased overall survival.In one embodiment, the percentage difference is a decrease of the MHDpatient's LDL particle size compared to the reference level or controlpopulation. In one embodiment, the decrease in the MHD patient's LDLparticle size compared to the reference level or control population isat least about 0.01, 0.1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,15, 16, 17, 18, 19, 20, 30, 40, 50, 60, 70, 80, 90, or 100% decrease. Inone embodiment, the decrease in the MHD patient's LDL particle sizecompared to the reference level or control population is at least about1, 2, 3, 4, 5, 6, 7, 8, 9, or 10% decrease. In one embodiment, thedecrease is at least about a 3% decrease in LDL particle size.

In one embodiment, LDL particle sizes equal to or less than about 230,225, 224, 223, 222, 221, 220, 219, 218, 217, 216, or 215 Å, areindicative of an increased risk of mortality, i.e., a decreased overallsurvival. In one embodiment, LDL particle sizes equal to or less thanabout 220, 219, 218, 217, or 216 Å, are indicative of an increased riskof mortality, i.e., a decreased overall survival.

In one aspect, predicting, determining, assessing, or assessment, in thecontext of MEM overall survival, including, lipid-related health risks,cardiovascular conditions, and risk of cardiovascular diseases, refersto a statistical correlation of the resulting LDL particle sizedistribution with population mortality and risk factors, as well knownin the art. In one embodiment, predicting, determining, assessing, orassessment, in the context of responsiveness to a therapeuticintervention, refers to comparison of the LDL particle size distributionbefore and after a therapeutic intervention is conducted.

MEM Prognosis Based on LDL Subfraction Concentration

Disclosed herein are methods for predicting overall survival in a MEMpatient based on LDL subfraction concentration, i.e., very small LDLand/or large LDL concentration. Further disclosed herein are methods formonitoring the status of MHD patients based on LDL subfractionconcentration, i.e., very small LDL and/or large LDL concentration. Inone embodiment, predicting the overall survival and monitoring thestatus of MHD patients requires a sample therefrom. The samplesdisclosed herein are represented by, but not limited to, whole blood,plasma, and/or serum. The present invention relates to methods forpredicating overall survival and monitoring the status of MHD patientsby comparing LDL subfraction concentration, i.e., very small LDL and/orlarge LDL concentration, in a sample from a patient to a reference levelor control population.

In one embodiment, a sample is obtained from a MHD patient.Subsequently, LDL subfraction concentration, i.e., very small LDL and/orlarge LDL concentration, is measured in the sample by ion mobilityanalysis. In one embodiment, overall survival or mortality risk is thendetermined by comparing the patient's LDL subfraction concentration,i.e., very small LDL and/or large LDL concentration, to a referencelevel or control population. In one embodiment, a difference between thepatient's LDL subfraction concentration, i.e., very small LDL and/orlarge LDL concentration, and the reference level indicates an increasedrisk of mortality. In one embodiment, a difference between the patient'sLDL subfraction concentration, i.e., very small LDL and/or large LDLconcentration, and the reference level does not indicate an increasedrisk of mortality. In one embodiment, a difference between the patient'sLDL subfraction concentration, i.e., very small LDL and/or large LDLconcentration, and the reference level indicates an increased chance ofoverall survival.

In one embodiment, the very small LDL concentration of the referencelevel or the control population is equal to or between about 40-130,50-125, or between 57-121 nmol/L. In one embodiment, the very small LDLconcentration of the reference level or the control population is equalto or between about 88-121 nmol/L. In one embodiment, the very small LDLconcentration of the reference level or the control population is aconcentration equal to or below about 100, 105, 110, 115, 120, 121, 125,130, or 135 nmol/L.

In one embodiment, a very small LDL concentration of the MHD patient ofat least about 110, 120, 130, 140, or 150 nmol/L, is indicative of anincreased risk of mortality, i.e., a decreased overall survival. In oneembodiment, a very small LDL concentration of the MHD patient of atleast about 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121,122, 125, 130, or 135 nmol/L, is indicative of an increased risk ofmortality, i.e., a decreased overall survival. In one embodiment, a verysmall LDL concentration of the MHD patient greater than about 121 nmol/Lis indicative of an increased risk of mortality, i.e., a decreasedoverall survival.

In one embodiment, the large LDL concentration of the reference level orthe control population is equal to or between about 30-120, 40-110, orbetween about 44-105 nmol/L. In one embodiment, the large LDLconcentration of the reference level or the control population is equalto or between about 77-105 nmol/L. In one embodiment, the largeconcentration of the reference level or the control population is aconcentration equal to or below about 105 nmol/L.

In one embodiment, a large LDL concentration of the MHD patient of atleast about 100, 110, 120, 130, 140, 150 nmol/L, is indicative of anincreased overall survival. In one embodiment, a large LDL concentrationof the MHD patient of at least about 100, 101, 102, 103, 105, or 106nmol/L, is indicative of an increased overall survival. In oneembodiment, a large LDL concentration of the MHD patient greater thanabout 105 nmol/L is indicative of an increased overall survival.

In one aspect, predicting, determining, assessing, or assessment, in thecontext of MEM overall survival, including, lipid-related health risks,cardiovascular conditions, and risk of cardiovascular diseases, refersto a statistical correlation of the resulting LDL subfractionconcentration, i.e., very small LDL and/or large LDL concentration, withpopulation mortality and risk factors, as well known in the art. In oneembodiment, predicting, determining, assessing, or assessment, in thecontext of responsiveness to a therapeutic intervention, refers tocomparison of the LDL subfraction concentration, i.e., very small LDLand/or large LDL concentration, before and after a therapeuticintervention is conducted.

In one aspect, the results of ion mobility LDL particle size, very smallLDL concentration, and large LDL concentration, analyses are reported inan analysis report. In one embodiment, an analysis report is reported toa clinician, other health care provider, epidemiologist, and the like.In one embodiment, the analysis report includes the results of analysisof a sample from a MEM patient. In one embodiment, an analysis reportmay include biochemical characterization of the LDL particle sizes, verysmall LDL concentration, and/or large LDL concentration, in the sample,in addition to other sample characteristics known in the art, e.g.,triglycerides, total cholesterol, LDL cholesterol, and/or HDLcholesterol, and the like. In one embodiment, an analysis report mayinclude characterization of lipoproteins, and references rangestherefore, conducted on samples prepared by the methods provided herein.In one embodiment, an analysis report includes LDL size distributionobtained via ion mobility analysis. In one embodiment, an analysisreport includes very small LDL concentration obtained via ion mobilityanalysis. In one embodiment, an analysis report includes large LDLconcentration obtained via ion mobility analysis. In one embodiment,reference level or control population levels are included in theanalysis report.

EXAMPLES

The present invention is further illustrated by the following examples,which should not be construed as limiting in any way. The present studyexamines which of the different aspects of conventional (triglycerides(TG) and cholesterol) and lipoprotein measurements (total particleconcentrations (Pc) including HDL-Pc and LDL-Pc, LDL particle diameter[LDL-Pd] and subfraction-Pc) can better identify MHD patients with anincreased death risk.

The examples described below illustrate methods for assessing riskhazard ratios using serological biomarkers. In MHD patients, traditionallipoprotein monitoring, such as measuring LDL levels, are not associatedwith cardiovascular events or mortality. However, direct determinationof lipoprotein particle sizes and subfraction concentrations arepossible via ion mobility analysis. Ion mobility analysis has beenpreviously described in U.S. Pat. App. Pub. Nos. 2008/0305549 and2008/0302666, and Otvos et al. (Clin. Chem. 54: 1307-1316, 2008). Ionmobility analysis provides for accurate and reproducible physicalmeasurement of both the size and concentration of a broad range ofcirculating lipoproteins. Additionally, measuring the concentrations ofdiverse subfractions of lipoproteins elucidates a means forrisk-stratification of multi-morbid individuals with chronic disease,e.g., MHD patients.

Lipoproteins were fractionated into very small, small, medium and largeLDL subfractions from archived baseline plasma samples using an ionmobility method. This method uses an ion separation/particle detectorsystem that fractionates lipoprotein particles from the small HDLparticles to the large very low density lipoprotein (VLDL) particles anddirectly counts each lipoprotein particle to permit the determination oflipoprotein particle concentration. Assay characteristics for the ionmobility are as follows: inter-assay variation for LDL-Pd was <1.0% forhigher concentration subfractions, HDL and LDL, the CV ranged from 13 to20% and for lower concentration subfractions, IDL and VLDL, CVs were 17to 30%. The diameter ranges used for each subfraction are as follows:HDL small, 76.5-105.0 Å; HDL large, 105.0-145.0 Å; LDL very small,180.0-208.2 Å; LDL small, 208.2-214.1 Å; LDL medium, 214.1-220.0 Å; LDLlarge, 220.0-233.3 Å; IDL small, 233.0-250.0 Å; IDL large, 250.0-296.0Å; VLDL small, 296.0-335.0 Å; VLDL medium, 335.0-424.0 Å; and VLDLlarge, 424.0-520.0 Å. The ratio of large/small LDL-Pc was calculatedusing the concentrations of the two subfractions in the sizes listedabove.

Example 1—Demographic, Clinical, and Laboratory Characterization of thePatient Population

In the examples below, stored sera from a 235 MEM patient cohort wasobtained and the 3- and 6-year survival rates were studied. The originalpatient cohort was derived over 5 years from a pool of over 3,000 MEMoutpatients. Included were outpatients who underwent MHD treatment forat least 8 weeks, who were 18 years or older and who signed theInstitutional Review Board approved consent form. Participants with ananticipated life expectancy <6 months (e.g. metastatic malignancy oradvanced AIDS) were excluded. A total of 893 MEM patients providedinformed consent to participate in the study. Approximately one-fourthof these patients (235 patients including 106 women) were randomlyselected to undergo additional tests including lipid profile and bodycomposition tests as described below.

Baseline demographic, clinical, and laboratory values in the 235 MHDpatients according to gender and BMI are shown in Table 1. The patients'mean age(±SD) was 54 (±14) years; 45% of patients were women (n=106) and26% (n=61) African-American. The median (interquartile range) ofdialysis vintage was 44 (29-71) months. TG, TC and LDL-C were highestamong women with high BMI and HDL-C was highest among women with lowBMI.

TABLE 1 Women (n = 106) Men (n = 129) p- Total BMI < 27 BMI >= 27 BMI <27 BMI >= 27 value N 235  57 49 80 49 Age (year) 54 ± 14 57 ± 14 56 ± 1551 ± 15 52 ± 13 0.06 BMI (kg/m²) 27.4 ± 6.8  22.8 ± 2.6  33.5 ± 6.0 23.3 ± 2.1  33.5 ± 7.1  <0.01 African Americans (%) 26 21 33 26 24 0.59Hispanic (%) 52 54 45 49 61 0.37 Diabetes mellitus (%) 58 47 67 55 670.09 Charlson comorbidity score 1.8 ± 1.5 1.7 ± 1.3 1.8 ± 1.4 1.8 ± 1.52.3 ± 1.6 0.20 Dialysis vintage (months) 44(29-71) 46(34-78) 34(23-73)47(29-74) 39(26-63) 0.21 Body fat (%) via NIR 45.0 ± 29.3 36.5 ± 25.975.6 ± 25.9 23.8 ± 10.7 58.6 ± 33.3 <0.01 Dialysis dose, Kt/V (sp) 1.67± 0.31 1.79 ± 0.30 1.70 ± 0.34 1.62 ± 0.29 1.56 ± 0.25 <0.01 Serumalbumin (mg/dl) 4.05 ± 0.45 3.92 ± 0.41 3.94 ± 0.32 4.15 ± 0.40 4.04 ±0.31 <0.01 creatinine (mg/dl) 9.7 ± 2.8 9.0 ± 2.7 8.9 ± 2.0 10.3 ± 3.0 10.4 ± 2.9  <0.01 CRP (mg/dl) 3.5(1.7-6.7) 2.2(1.1-5.0) 4.7(2.7-7.3)2.9(1.3-5.8) 4.7(2.7-7.8) <0.01 IL-6(mg/dl)  6.0(3.8-12.0) 4.9(3.4-11.4)  8.0(4.4-10.9)  5.9(3.4-12.9)  7.3(4.2-12.8) 0.32 TNF-a(mg/dl) 3.5(2.4-4.6) 3.5(1.9-4.6) 3.7(2.1-4.5) 3.5(2.6-4.4) 3.5(2.5-5.1)0.86 Conventional lipid measurements triglyceride (mg/dl) 153 ± 11  118± 52  196 ± 113 133 ± 86  180 ± 160 <0.01 total cholesterol (mg/dl) 143± 42  135 ± 37  166 ± 50  133 ± 34  146 ± 42  <0.01 LDL-C (mg/dL) 76 ±29 68 ± 26 88 ± 29 72 ± 28 80 ± 32 <0.01 HDL-C (mg/dL) 37 ± 12 43 ± 1537 ± 14 36 ± 10 32 ± 9  <0.01 Alternative LDL particle measures totalLDL-Pc (nmol/L) 255 ± 154 215 ± 156 299 ± 171 241 ± 149 281 ± 162 0.02very small LDL-Pc (nmol/L) 101 ± 66  87 ± 46 119 ± 78  95 ± 63 111 ± 73 0.04 small LDL-Pc (nmol/L) 30 ± 22 23 ± 13 38 ± 29 27 ± 17 37 ± 27 <0.01medium LDL-Pc (nmol/L) 34 ± 25 27 ± 19 42 ± 30 32 ± 23 40 ± 27 <0.01large LDL-Pc (nmol/L) 89 ± 64 79 ± 65 100 ± 69  87 ± 62 94 ± 57 0.34LDL-Pd (angstrom) 215 ± 10  217 ± 10  213 ± 10  217 ± 10  214 ± 10  0.06Alternative HDL particle measures total HDL-Pc (nmol/L) 2773 ± 3500 2921± 3805 2324 ± 1873 3038 ± 4084 2616 ± 3387 0.69 small HDL-Pc (nmol/L)2228 ± 3309 2328 ± 3575 1777 ± 1768 2511 ± 3852 2101 ± 3252 0.66 largeHDL-Pc (nmol/L) 566 ± 399 603 ± 426 547 ± 377 583 ± 432 514 ± 331 0.66All values are presented as mean ± SD or percentages except forvariables that are not normally distributed (vintage, CRP, IL6 and TNFalpha) which we used interquartile range(IQR). All analysis are ANOVAexcept for variables that are not normally distributed (vintage, CRP,IL6 and TNF alpha) which we used Kruskal-Wallis test. Abbreviations:LDL, Low density lipoprotein; LDL-C, LDL cholesterol; LDL-Pc, LDLparticle concentration: LDL-Pd, LDL particle diameter; HDL, high densitylipoprotein; HDL-C, HDL cholesterol;; TG, triglyceride; CRP, C-reactiveprotein; IL-6, interleukin-6; TNF-a, tumor necrosis factor-alpha; BMI,body mass index.

Example 2—Correlation of Lipid Fractions and Other PhysiologicalVariables

Table 2 shows the correlation coefficients of relevant measures with thesubfractions of LDL-Pc and HDL-Pc. The four conventional lipid measures,serum TC, LDL-C, HDL-C and TG, were correlated with all subfractions ofLDL-Pc. HDL-C was associated negatively with small and medium LDL-Pc andpositively with large HDL-Pc. LDL-Pd was correlated positively with verysmall and large LDL-Pc and negatively with small HDL-Pc. Scatter plotsof correlations of conventional serum LDL-C and HDL-C with alternativelarge LDL-Pc and large HDL-Pc concentrations indicated correlationcoefficients of r=0.34 (p<0.01) and r=0.23 (p<0.01), respectively (FIGS.1A and 1B).

TABLE 2 LDL particle measures HDL particle measures Very small SmallMedium Large Small Large LDL-Pc LDL-Pc LDL-Pc LDL-Pc HDL-Pc HDL-Pc Age0.05 −0.04 0.02 −0.01 −0.17* 0.08 Vintage −0.11 −0.13* −0.11 −0.10 0.05−0.06 Charlson comorbidity 0.01 −0.02 0.01 0.02 0.05 0.00 score BMI0.18** 0.17** 0.16* 0.08 0.00 −0.02 NIR Fat mass percent 0.21** 0.090.07 0.03 0.01 0.00 TG 0.39** 0.43** 0.43** 0.33** 0.15 0.01 TC 0.33*0.36** 0.38** 0.37** 0.09 0.01 LDL-C 0.21** 0.28** 0.29** 0.33** 0.04−0.06 HDL-C −0.07 −0.23** −0.21** −0.15* −0.07 0.14* Total LDL-Pc 0.87**0.96** 0.93** 0.89** 0.34** 0.35** Very small LDL-Pc — 0.87** 0.77**0.66** 0.41** 0.33** Small LDL-Pc 0.87** — 0.95** 0.84** 0.35* 0.25**Medium LDL-Pc 0.77** 0.95** — 0.93** 0.27** 0.24** Large LDL-Pc 0.66**0.84** 0.93** — 0.25** 0.29** Total HDL-Pc 0.55** 0.29** 0.21** 0.17*0.85** 0.52** Small HDL-Pc 0.41* 0.35** 0.27** 0.01 — 0.11 Large HDL-Pc0.33** 0.25** 0.24** 0.25** 0.11 — LDL-Pd 0.31** −0.11 −0.02 0.29**−0.20** −0.09 CRP −0.01 0.00 0.00 −0.06 −0.01 −0.09 IL-6 −0.11 −0.12−0.15* −0.14* 0.00 0.00 TNF-a −0.06 0.06 −0.04 −0.03 0.02 −0.17**Dietary data Energy intake 0.03 0.00 −0.06 −0.09 −0.16 0.04 SAFA intake0.04 0.02 −0.07 −0.10 −0.15 0.07 MUFA intake −0.02 −0.01 −0.09 −0.12−0.11 0.01 PUFA intake −0.10 −0.13 −0.20 −0.19 −0.13 −0.03 SGA −0.12−0.16* −0.15* −0.12 −0.03 −0.02 *p < 0.05, **p < 0.01 (r values >= 0.20are bold)

Example 3—Correlation of Lipid Fractions with Mortality

Over the six years of the cohort 71 MHD patients (31%) died. The deathhazard ratio (HR) across the quartiles of conventionally measured serumLDL-C and HDL-C and alternative LDL-Pc and HDL-Pc was calculated. Asshown in Table 3 no association was observed between LDL-C, HDL-C orLDL-Pc and mortality in MHD patients. However, the highest quartile ofthe total HDL-Pc was associated with 2.2-fold higher death risk. Verylow density lipoprotein (VLDL) and intermediate density lipoprotein(IDL) cholesterol concentrations were not associated with increased ordecreased mortality either (data not shown).

The mortality-predictabilities of alternative lipid-Pc and LDL-Pdmeasures were examined by calculating the death hazard ratio (HR) acrosstheir quartiles, highest vs. lowest. Among these subfractions of LDL-P,the highest concentrations of very small and large LDL-P were associatedwith highest and lowest mortality, respectively, especially afteradjustment for case-mix, conventional lipids, MICS and inflammation(Table 4). No association was observed between alternative small andlarge HDL-Pc and mortality (Table 2).

The death HRs were also calculated for the quartiles of LDL-Pd (Table 6)and large/small LDL-Pc ratio (Table 2). There was no significantassociation in the unadjusted models. However, both measures wereassociated with decreased mortality after adjustment for case-mix,conventional lipids, MICS and inflammation. The death HRs (1st to 4thquartiles) for quartiles of LDL-Pd were 1.0, 0.93(0.46-1.87),0.43(0.21-0.89), and 0.45(0.31-1.00); and for quartiles of thelarge/small LDL-Pc ratio 1.0, 0.64(0.31-1.32), 0.51(0.25-1.02), and0.43(0.20-0.95), respectively (Table 5). These relationships wereverified in cubic spline analyses examining Cox based multivariateadjusted association between smaller LDL-Pd and higher mortality (FIGS.2A and 2B). Hence, in Cox based multivariate adjusted analysis smallerLDL-Pd was associated with higher mortality. The net reclassificationimprovement for LDL particle diameter, very small and large LDL wascalculated and found to be 0.05(p=0.25), 0.22(p<0.01) and 0.03 (p=0.47),respectively.

TABLE 3 Q1 Q2 Q3 Q4 P-for- Conventional LDL (n = 62) (n = 58) (n = 59)(n = 56) trend LDL-C (mg/dl) <55 55-72 73-94 >94 Unadjusted 1 1.25(0.66-2.37) 0.88 (0.44-1.78) 1.16 (0.61-2.20) 0.90 Case-mix¹ + lipids² 11.11 (0.56-2.19) 0.98 (0.47-2.05) 1.21 (0.58-2.50) 0.71 Previous + MICS³⁺ 1 1.41 (0.68-2.89) 1.16 (0.52-2.58) 1.43 (0.65-1.15) 0.49inflammation⁴ Q1 Q2 Q3 Q4 Conventional HDL (n = 64) (n = 55) (n = 62) (n= 54) HDL-C (mg/dl) <29 29-34 35-44 >44 Unadjusted 1 0.95 (0.48-1.92)1.10 (0.58-2.13) 1.52 (0.80-2.91) 0.19 Case-mix + lipids 1 0.77(0.37-1.62) 0.92 (0.44-1.90) 0.85 (0.40-1.81) 0.80 Previous + MICS ⁺ 10.92 (0.41-2.02) 1.19 (0.52-2.74) 0.99 (0.43-2.23) 0.93 inflammationAlternative LDL Q1 Q2 Q3 Q4 p-for- particle (n = 60) (n = 59) (n = 58)(n = 58) trend Total LDL-Pc <144 144-215 216-315 >315 (nmol/L)Unadjusted 1 0.89 (0.46-1.73) 0.93 (0.47-1.82) 0.87 (0.45-1.69) 0.72Case-mix + lipids 1 0.76 (0.37-1.55) 1.29 (0.61-2.73) 1.04 (0.48-2.27)0.60 Previous + MICS ⁺ 1 0.46 (0.20-1.04) 1.36 (0.62-2.98) 0.84(0.35-2.03) 0.65 inflammation Alternative HDL Q1 Q2 Q3 Q4 particle (n =59) (n = 59) (n = 59) (n = 58) Total HDL-Pc <936 936-14661467-2919 >2919 (nmol/L) Unadjusted 1 0.86 (0.42-1.77) 1.03 (0.52-2.08)1.50 (0.78-2.87) 0.18 Case-mix + lipids 1 1.00 (0.47-2.11) 1.58(0.76-3.27) 1.69 (0.86-3.32) 0.07 Previous + MICS ⁺ 1 1.05 (0.48-2.29)1.44 (0.67-3.11) 2.22 (1.02-4.81)* 0.03 inflammation ¹Case-mix includedage, gender, race/ethnicity, diabetes mellitus, dialysis vintage ,modified Charlson comorbidity score and dialysis dose (single poolKt/V). ²Lipids included total LDL and HDL particles concentrations andtriglyceride. ³Malnutrition-inflammation complex syndrome (MICS)variables included serum or blood levels of phosphorus, albumin,creatinine, calcium, ferritin, hemoglobin, normalized protein catabolicrate (nPCR), also known as normalized protein nitrogen appearance(nPNA); and body mass index. ⁴Inflammatory markers include serumconcentrations of C-reactive protein, interleukin-6, and tumor necrosisfactor-α. *Significant values are in bold (p < 0.05) **P for interactionpertains to malnutrition-inflammation complex was not significant in anyof the models (>0.19), >0.33 LDL, >0.10 conventional HDL, alternativeLDL and alternative HDL particle models respectively) Death hazardratios are provided as mean values with 95% confidence intervalsAbbreviations: LDL, Low density lipoprotein; LDL-C, LDL cholesterol;LDL-Pc, LDL particle concentration; HDL, high density lipoprotein;HDL-C, HDL cholesterol.

TABLE 4 Q1 Q2 Q3 Q4 P-for- Very small LDL-P (n = 58) (n = 60) (n = 59)(n = 58) trend Very small LDL-Pc (nmol/L) <57 57-87 88-121 >121Unadjusted 1 0.84 (0.42-1.70) 0.93 (0.45-1.91) 1.54 (0.82-2.89) 0.13Case-mix¹ + lipids² 1 0.90 (0.43-1.90) 1.18 (0.53-2.59) 2.44(1.10-5.44)* 0.02 Previous + MICS³ ⁺ 1 0.67 (0.29-1.55) 0.88 (0.38-2.05)2.43 (1.03-5.72)* 0.03 inflammation⁴ Q1 Q2 Q3 Q4 Small LDL-P (n = 62) (n= 60) (n = 59) (n = 54) Small LDL-Pc (nmol/L) <15 15-24 25-36 >36Unadjusted 1 1.57 (0.83-2.97) 1.68 (0.56-2.34) 0.92 (0.45-1.89) 0.63Case-mix + lipids 1 1.60 (0.84-3.06) 1.43 (0.69-2.93) 1.40 (0.61-3.19)0.43 Previous + MICS ⁺ 1 1.36 (0.67-2.73) 1.73 (0.82-3.61) 1.23(0.49-3.12) 0.41 inflammation Q1 Q2 Q3 Q4 Medium LDL-P (n = 64) (n = 55)(n = 57) (n = 59) Medium LDL-Pc (nmol/L) <17 47-26 27-46 >46 Unadjusted1 1.42 (0.74-2.72) 1.32 (0.67-2.58) 0.87 (0.43-1.78) 0.65 Case-mix +lipids 1 1.50 (0.76-2.97) 1.64 (0.80-3.33) 1.17 (0.51-2.68) 0.60Previous + MICS ⁺ 1 1.24 (0.60-2.58) 2.07 (0.98-4.37) 1.15 (0.46-2.91)0.37 inflammation Q1 Q2 Q3 Q4 Large LDL-P (n = 63) (n = 55) (n = 59) (n= 58) Large LDL-Pc (nmol/L) <44 44-76 77-105 >105 Unadjusted 1 0.70(0.37-1.34) 0.88 (0.47-1.62) 0.45 (0.22-0.92)* 0.05 Case-mix + lipids 10.63 (0.31-1.28) 0.68 (0.34-1.37) 0.37 (0.16-0.87)* 0.04 Previous + MICS⁺ 1 0.51 (0.23-1.12) 0.96 (0.45-2.05) 0.38 (0.15-0.96)* 0.13inflammation ¹Case-mix included age, gender, race/ethnicity, diabetesmellitus, dialysis vintage , modified Charlson comorbidity score anddialysis dose (single pool Kt/V). ²Lipids included LDL and HDLcholesterol concentrations and triglyceride plus large LDL and verysmall LDL in the analysis of quartiles of very small LDL and large LDLrespectively. ³Malnutrition-inflammation complex syndrome (MICS)variables included serum or blood levels of phosphorus, albumin,creatinine, calcium, ferritin, hemoglobin, normalized protein catabolicrate (nPCR), also known as normalized protein nitrogen appearance(nPNA); and body mass index. ⁴Inflammatory markers include serumconcentrations of C-reactive protein, interleukin-6, and tumor necrosisfactor-α. *Significant values are in bold (p < 0.05) **P for interactionpertains to malnutrition-inflammation complex was not significant in anyof the models (see text) Death hazard ratios are provided as mean valueswith 95% confidence intervals Abbreviations: LDL, Low densitylipoprotein; LDL-Pc, LDL particle concentration.

TABLE 5 Q1 Q2 Q3 Q4 P-for- P for Small HDL-P (n = 59) (n = 60) (n = 58)(n = 58) trend interaction ** Small HDL-Pc (nmol/L) <494 494-10691070-2538 >2538 Unadjusted 1 0.71(0.34-1.45) 1.09(0.56-2.14)1.28(0.67-2.45) 0.17 0.51 Case-mix₁ 1 0.78(0.38-1.62) 1.38(0.70-2.73)1.34(0.70-2.58) 0.09 0.57 Case-mix + 1 0.70(0.33-1.46) 1.39(0.69-2.79)1.36(0.69-2.66) 0.15 0.33 lipids₂ Previous + 1 0.66(0.31-1.41)1.04(0.50-2.17) 1.58(0.77-3.27) 0.13 0.58 MICS₃ Previous + 10.64(0.30-1.37) 1.00(0.48-2.10) 1.54(0.74-3.21) 0.15 0.58 inflammation₄Q1 Q2 Q3 Q4 Large HDL-P (n = 59) (n = 59) (n = 58) (n = 59) Large HDL-Pc(nmol/L) <304 304-473 474-685 >685 Unadjusted 1 1.21(0.57-2.54)1.69(0.84-3.40) 1.92(0.97-3.80) 0.03 0.78 Case-mix₁ 1 1.38(0.66-2.95)1.73(0.85-3.52) 1.83(0.92-3.67) 0.79 0.90 Case-mix + 1 1.28(0.69-2.79)1.49(0.73-3.06) 1.24(0.56-2.70) 0.64 0.99 lipids₂ Previous + 11.59(0.70-3.61) 1.59(0.74-3.44) 1.40(0.51-3.21) 0.66 0.85 MICS₃Previous + 1 1.66(0.76-3.79) 1.55(0.71-3.73) 1.42(0.62-3.27) 0.66 0.83inflammation₄ Large/small Q1 Q2 Q3 Q4 LDL-P ratio₅ (n = 59) (n = 59) (n= 59) (n = 58) Large/small LDL-Pc <0.44 0.44-0.68 0.69-0.97 >0.97Unadjusted 1 0.59(0.31-1.13) 0.63(0.34-1.19) 0.62(0.33-1.17) 0.17 0.83Case-mix₁ 1 0.52(0.27-1.01) 0.63(0.33-1.18)  0.48(0.24-0.97)* 0.06 0.94Case-mix + 1 0.46(0.23-0.92)  0.52(0.27-1.00)*  0.41(0.20-0.87)* 0.030.89 lipids₂ Previous + 1 0.63(0.31-1.28) 0.52(0.26-1.05) 0.43(0.20-0.93)* 0.02 0.83 MICS₃ Previous + 1 0.64(0.31-1.32)0.51(0.25-1.02)  0.43(0.20-0.95)* 0.02 0.87 inflammation₄ ₁Case-mixincluded age, gender, race/ethnicity, diabetes mellitus, dialysisvintage, modified Charlson comorbidity score and dialysis dose (singlepool Kt/V). ₂Lipids included LDL and HDL cholesterol concentrations andtriglyceride. ₃Malnutrition-inflammation complex syndrome (MICS)variables included serum or blood levels of phosphorus, albumin,creatinine, calcium, ferritin, hemoglobin, normalized protein catabolicrate (nPCR), also known as normalized protein nitrogen appearance(nPNA); and body mass index. ₄Inflammatory markers include serumconcentrations of C-reactive protein, interleukin-6, and tumor necrosisfactor-α. ₅The cutoffs for the quartiles of the large/small LDL-Pc ratiowere 0.11-0.44, 0.45-0.68, 0.69-0.96 and 0.97-1.74 for Quartiles 1 to 4,respectively. Death hazard ratios are provided as mean values with 95%confidence intervals *Significant values are in bold (p < 0.05) ** P forinteraction pertains to malnutrition-inflammation complex Abbreviations:LDL, Low density lipoprotein; LDL-Pc, LDL particle concentration;;HDL-Pc; HDL particle concentration

TABLE 6 P- LDL particle diameter Q1 Q2 Q3 Q4 for- (LDL-Pd) quartiles (n= 61) (n = 57) (n = 59) (n = 58) trend LDL particle diameter (Å) <211.4211.4-216.4 216.5-222.1 >222.8 Unadjusted 1 0.76 (0.40-1.43) 0.65(0.34-1.23) 0.70 (0.37-1.33) 0.22 Case-mix¹ + lipids² 1 0.97 (0.50-1.86)0.49 (0.25-0.96)* 0.52 (0.25-1.09) 0.03 Previous + MICS³ ⁺ 1 0.93(0.46-1.87) 0.43 (0.21-0.89)* 0.45 (0.31-1.00)* 0.02 inflammation⁴¹Case-mix included age, gender, race/ethnicity, diabetes mellitus,dialysis vintage , modified Charlson comorbidity score and dialysis dose(single pool Kt/V). ²Lipids included LDL and HDL cholesterolconcentmtions and triglyceride ³Malnutrition-inflammation complexsyndrome (MICS) variables included serum or blood levels of phosphorus,albumin, creatinine, calcium, ferritin, hemoglobin, normalized proteincatabolic rate (nPCR), also known as normalized protein nitrogenappearance (nPNA); and body mass index. ⁴Inflammatory markers includeserum concentrations of C-reactive protein, interleukin-6, and tumornecrosis factor-α. Death hazard ratios are provided as mean values with95% confidence intervals *Significant values are in bold (p <0.05)

Example 4—Risk Stratification of MHD Patients Using Alternate LipidMeasurements

In order to investigate whether the alternative lipid measures describedherein can help better risk-stratify MHD patients, we examined themortality predictability of the combinations of LDL-Pd with conventionalLDL-C by dichotomizing all subjects into below-median vs. above-medianLDL-C (median: 73 mg/dL) as well as below-median vs. above-median LDL-Pd(median: 216.5 Å), leading to four (2×2) mutually exclusive groups. Asshown in FIG. 3, above-median LDL-C combined with above-median LDL-Pdwas associated with the lowest death risk. FIG. 3 illustrates thestatistical interactions, in that above-median vs. below-median serumLDL-C appeared paradoxically protective in the context of above-medianLDL-Pd but within the below-median LDL-Pd, above-median serum LDL-C wasassociated with a trend towards a 47% higher death risk. FIG. 4 showsthe Kaplan-Meier proportion of surviving according to the fouraforementioned categories of LDL-Pd and LDL-C concentration, which wereconsistent with the Cox models. We also implemented the same 2×2approach to examine the mortality-predictability of the fourcombinations of total LDL-Pc and LDL-Pd by dichotomizing each intoabove-median versus below-median. Median value for total LDL-Pc=216nmol/L. We also calculated death hazard ratios of conventional LDL-C,HDL-C and alternative total LDL-P concentrations across above-median andbelow-median values of alternative LDL-P diameter, in that median valueswere used to dichotomize each measure forming a 2-by-2 table. Aftermultivariate adjustments above-median total LDL-Pc combined withabove-median LDL-Pd was associated with the lowest death risk, i.e. 74%lower mortality compared to below-median total LDL-Pc combined withbelow-median LDL-Pd. However, p-values -for-interaction with LDL-Pd werenot significant (>0.17).

Summary of Results and Discussion

We examined the mortality predictability of both traditional andalternative measures of lipoproteins and their particle and subfractionconcentrations, including LDL-Pd, in a cohort of 235 MHD patients whowere followed for up to 6 years. It was discovered that non-traditionallipoprotein measures could better predict outcomes of MHD patients.Prior studies have indicated a lipid paradox in dialysis patients, inthat lower serum TC and LDL-C are paradoxically associated with higherdeath risk. Hence, alternative lipoprotein measures may more accuratelyreflect the increased cardiovascular risk in this patient population.

The present studies demonstrate that conventional TC, LDL-C and HDL-Cwere not able to predict mortality, consistent with the previousliterature in CKD and CHF patient population. Higher HDL-Pc wasassociated with higher death risk. Prognostic factors for survival inthese patients included LDL-P subfraction concentrations and LDL-Pd.Decreased LDL-Pd, indicating smaller LDL particle size, was associatedwith an increased death risk even after adjustment for demographics,comorbidities, and conventional measurements of lipids, nutritionalstatus and inflammation. Higher concentrations of very small LDL-P wereassociated with higher mortality, whereas higher concentrations of largeLDL-P were associated with greater survival. The present studies alsodemonstrate that larger (above-median) LDL-Pd with either above-medianor below-median total LDL-Pc was associated with greater survival. Inpatients with below-median LDL-Pd, the highest risk of mortality wasassociated with above-median levels of LDL-C. However, above-medianLDL-Pc combined with below-median LDL-Pd had a lower risk of mortalitythan below-median LDL-Pd and below-median LDL-Pc. There is a strongtrend in patients with below-median LDL-Pd, above-median LDL-C andabove-median LDL-Pc showing increased and decreased risk, respectively.This indicates that the LDL-C content of the LDL particles in thispopulation may not be proportional to the LDL-Pd. It is generallyassumed that larger the LDL particles contain more cholesterol. However,the present data indicates a difference in particle composition indialysis patients. It was also found that larger LDL-Pd with eitherabove-median or below-median total HDL-C tended to correlate withgreater survival.

Conventional chemical measures of lipid concentration have long been themost used clinical measurements of lipid profile. However, in chronicdisease states such as CKD and CHF these measures do not appear topredict outcomes. Total LDL-Pc and LDL-Pd show better correlation withatherosclerotic progression and cardiovascular events than conventionalLDL-C. Indeed, conventional LDL-C can be low, yet total LDL-Pc may behigh and cardiovascular events rates may be increased. Conversely,conventional LDL-C can be high, yet total LDL-Pc low especially in thesetting of low cardiovascular risk. These scenarios are more likely tobe the case in chronic disease states such as CKD and CHF whereconventional lipid concentrations may be confounded by wasting syndromeand MICS.

Conventional lipids measurements such as LDL-C and HDL-C have not provento be of great assistance in MHD patients in assessing cardiovascular ordeath risk. This was also observed in the present study in which themeasurement of LDL-Pd and LDL-P subfraction concentration betteridentified MHD individuals at increased risk of death for up to 6 yearsthereafter. The association of LDL-Pd and its subfraction concentrationswith death was independent of conventional LDL-C and HDL-C orinflammation. In the present study, adjustments for case-mix and lipidsas well as MICS increased the robustness of the ion mobility measuredalternative LDL parameter for predicting mortality demonstrating thatthe LDL-Pd and subfraction concentrations are superior predictors ofmortality independent of conventional lipid measurements.

The present invention is not to be limited in terms of the particularembodiments described in this application. Many modifications andvariations can be made without departing from its spirit and scope, aswill be apparent to those skilled in the art. Functionally equivalentmethods and apparatuses within the scope of the disclosure, in additionto those enumerated herein, will be apparent to those skilled in the artfrom the foregoing descriptions. Such modifications and variations areintended to fall within the scope of the appended claims. The presentinvention is to be limited only by the terms of the appended claims,along with the full scope of equivalents to which such claims areentitled. It is to be understood that this disclosure is not limited toparticular methods, reagents, compounds compositions or biologicalsystems, which can, of course, vary. It is also to be understood thatthe terminology used herein is for the purpose of describing particularembodiments only, and is not intended to be limiting.

In addition, where features or aspects of the disclosure are describedin terms of Markush groups, those skilled in the art will recognize thatthe disclosure is also thereby described in terms of any individualmember or subgroup of members of the Markush group.

As will be understood by one skilled in the art, for any and allpurposes, particularly in terms of providing a written description, allranges disclosed herein also encompass any and all possible subrangesand combinations of subranges thereof. Any listed range can be easilyrecognized as sufficiently describing and enabling the same range beingbroken down into at least equal halves, thirds, quarters, fifths,tenths, etc. As a non-limiting example, each range discussed herein canbe readily broken down into a lower third, middle third and upper third,etc. As will also be understood by one skilled in the art all languageincluding, but not limited to, e.g., “up to,” “at least,” “greaterthan,” “less than,” and the like include the number recited and refer toranges which can be subsequently broken down into subranges as discussedabove. Finally, as will be understood by one skilled in the art, a rangeincludes each individual member.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopeand spirit being indicated by the following claims.

What is claimed is:
 1. A method for predicting mortality risk in amaintenance hemodialysis (MHD) patient comprising: a) separatinglipoprotein particles from non-lipoprotein particles in a plasma samplefrom an MHD patient; b) irradiating with alpha radiation andfractionating the lipoprotein particles according to particle diameterusing ion mobility lipoprotein fractionation; c) separating thefractionated lipoprotein particles into one or more subfractionsaccording to the following ranges: (i) 76.5-105.0 Åas high densitylipoprotein (HDL) small, (ii) 105.0-145.0 as ÅHDL large; (iii)180.0-208.2 Åas low density lipoprotein (LDL) very small (vs-LDL), (iv)208.2-214.1 Åas LDL small, (v) 214.1-220.0 Åas LDL medium, (vi)220.0-233.3 Åas LDL large (l-LDL), (vii) 233.0-250.0 Åand intermediatedensity lipoprotein (IDL), (viii) 250.0-296.0 Åas IDL large, (ix)296.0-335.0 Åas very low density lipoprotein (VLDL) small, (x)335.0-424.0 Åas VLDL medium, and (xi) 424.0-520.0 Åas VLDL large; d)measuring the concentration of lipoprotein particles in each subfractionof (c); and e) determining the patient as having increased risk ofmortality when the sample vs-LDL particle concentration is greater thana reference vs-LDL particle concentration, the sample l-LDL particleconcentration is lower than a reference l-LDL particle concentration, ora combination thereof.
 2. The method of claim 1, wherein the referencevs-LDL particle concentration level is about 121 nmol/L.
 3. The methodof claim 1, wherein the reference vs-LDL particle concentration isderived from a control population of subjects not undergoing maintenancehemodialysis.
 4. The method of claim 1, wherein the reference vs-LDLparticle concentration is the vs-LDL particle concentration measured inthe patient at an earlier time.
 5. The method of claim 1, wherein thereference vs-LDL particle concentration is the vs-LDL particleconcentration measured in the patient prior to receiving treatment. 6.The method of claim 1, wherein reference l-LDL particle concentration isabout 105 nmol/L.
 7. The method of claim 1, wherein the reference l-LDLparticle concentration is derived from a control population of subjectsnot undergoing maintenance hemodialysis.
 8. The method of claim 1,wherein the reference l-LDL particle concentration is the l-LDLconcentration measured in a plasma sample from the patient at an earliertime.
 9. The method of claim 1, wherein the reference l-LDL particleconcentration is the l-LDL concentration measured in a plasma samplefrom the patient prior to receiving treatment.
 10. The method of claim1, further comprising determining a l-LDL:s-LDL ratio.
 11. The method ofclaim 10, further comprising comparing the l-LDL:s-LDL ratio to areference l-LDL:s-LDL ratio.
 12. The method of claim 11, wherein thereference l-LDL:s-LDL ratio is about 0.90-1.05.
 13. The method of claim11, wherein the reference l-LDL:s-LDL ratio is derived from a controlpopulation of subjects not undergoing maintenance hemodialysis.
 14. Themethod of claim 11, wherein the reference l-LDL:s-LDL ratio is thel-LDL:s-LDL ratio measured in a plasma sample from the patient at anearlier time.
 15. The method of claim 11, wherein the referencel-LDL:s-LDL ratio is the l-LDL:s-LDL ratio measured in a plasma samplefrom the patient prior to receiving treatment.
 16. The method of claim1, further comprising measuring the total concentration of low densitylipoprotein (LDL-C) and/or the mean low density lipoprotein diameter(LDL-Pc) in the plasma sample.
 17. The method of claim 1, furthercomprising measuring the total concentration of high density lipoprotein(HDL-C) and/or the mean high density lipoprotein diameter (HDL-Pc) inthe plasma sample.